Algorithmic Steps

  1. Create a live script file called “run_digit_recognition.mlx”. Clear workspace and then load the data. When you load the data, you will observe 2 variables named images and labels being created in workspace. 2. Visualize every 500th image (1,501, 1001…etc.) to visualize each digit using imagesc() and gray color map. Try to check the labels for those cases. 3. Now, split the dataset into a set of training and testing data. Use 60th image of each digit for testing (1, 61, 121 and so on) and rest of the dataset is used for training Make sure the size of testing images and training images are 20 × 20 × 84 and 20 × 20 × 4916 respectively. 4. Make sure to note the labels of training and testing data as well. Note that train labels and test labels are of size 4916 × 1 and 84 × 1 respectively. 5. Now, write a nested loop in which you compute the Euclidean distance of each test image from all training images. So distance would be a vector of size 4916 × 1 for each test image. 6. Later, determine the case with minimum Euclidean distance for each test image and note the corresponding index. 7. Determine the label with minimum Euclidean distance and that represents your predicted label. Repeat this process for all test images.